Python#

If you don’t already have python, you can follow instructions on the Software Carpentry website. Note that their instructions are based on using Anaconda.

Python packages#

Installing packages (Windows)#

Ok, we are presuming some previous knowledge, but to help you get past some hurdles experienced, see tips below:

  • If you try to run some code with an “import” line, and you get an error, this is likely because you don’t have the package installed (on your computer) and python is trying to load it.

Some packages you will need:

  • Gibbs Seawater Toolbox (GSW) - see installation instructions here

  • xarray - see installation for xarray and necessary other packages (dask, netCDF4, bottleneck). This is a very useful data format/package for netcdf files (many ocean/climate datasets).

  • pyGMT - a plotting package (like matplotlib and cartopy. Please note, installation is recommended via a package/environment manager like conda. If you are using pip to install, then read the troubleshooting section below as well.

You may also need:

  • pandas - simpler than xarray. Avoid, but may be needed.

  • numpy - multi-dimensional arrays

  • matplotlib - plotting tools, similar to Matlab style

  • pycnv - for importing Seabird format *.cnv datafiles

Potentially of interest:

  • ocean data parser - but only has pip install instructions here. If you’ve been using conda and you’d like to use pip instead, you can use conda to install pip. See some info here

Installing packages (Mac)#

We need various packages as above. For environment management, we use conda or mamba, and their lightweight forms: miniconda and micromamba. Some students also use pipenv, which you can explore on your own e.g. here.

See also

Managing environments with conda or mamba.

Choose a directory where you will work on the python for this course. Navigate to the directory in a terminal window. The basic command is something like conda create --name <my-env> where you replace <my-env> with the name of your environment. Let’s call it seaocn_env. But we’ll try to install a lot of the necessary pieces at once:

conda create --channel conda-forge --name seaocn_env xarray gsw python pandas gsw dask netCDF4 bottleneck numpy matplotlib jupyterlab nb_conda jupyter-book ipykernel nb_conda_kernels pygmt cartopy scipy
conda activate seaocn_env

Note

To install an individual package with conda, you can use

conda install -c conda-forge <package>

We need a few more packages that seem to only have pip instructions

pip install pycnv
pip install git+https://github.com/cioos-siooc/ocean-data-parser.git
conda env export > environment.yml

This has created a list of the current environment which you can get from this repository environment.yml (repository). If you download this into your working directory, you can activate the environment:

conda env create -f environment.yml

Then before you start working, activate the environment using

conda activate seaocn_env
jupyter-lab

Now, when you start jupyter-lab you’ll see that you can select the environment seaocn_env. Or, when you open an existing python notebook (*.ipynb file), in the upper right corner you can select the environment (e.g., seaocn_env and tick the box to always open with preferred kernel).

Note

If it’s been a while since you installed conda, you can update it with

conda update -n base -c conda-forge conda

But your conda will tell you when you need to do this

Note

If you want to recreate your environment with conda, after adding a few things, you can do a

conda deactivate
conda remove --name seaocn_env --all

And then start fresh with the conda create code above, appending the extra packages in the intial install.

Problems with jupyter-book and kernels#

My jupyter-book didn’t know about my environment seaocn_env. This should not be a problem for students, but is a problem when i want to execute this book (the course notes) which contain packages that aren’t in my base installation of python on my computer.

pico $HOME/.jupyter/jupyter_config.json

and add the lines

{
  "CondaKernelSpecManager": {
    "kernelspec_path": "--user"
  }
}

After saving this, I was able to run

python -m nb_conda_kernels list

and it appeared to add my kernels.

Then making the jupter book worked without errors.

See also

https://fcollonval.medium.com/conda-environments-in-jupyter-ecosystem-without-pain-e9fab3992fb7

(seaocn_env) 18:20 ~/Library/Mobile Documents/com~apple~CloudDocs/Work/teaching/SeaOcn-UHH $ python -m nb_conda_kernels list

Environments#

More advanced: It’s best to work inside an environment. This controls the version of each of the packages that you have installed, so that the code is more likely to run smoothly on another person’s computer. Some background on conda environments.

ADCP data#

This is a stub: Likely looking package for handling *.000 ADCP data files. pycurrents_ADCP_processing.